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Data Anonymization


Today’s data contains personally identifiable information (PII), which has to be protected. The protection methods include strong encryption, authentication and authorization, and anonymization. The latter, the data anonymization, removes or modifies sensitive data in an irreversible transformation, preserving sufficient data characteristics to enable analysis, aggregations and evaluation while protecting privacy. In our talk we will discuss using two modules: faker and mimesis, for anonymizing customer data. The primarily goal of both modules is producing synthetic data for development and quality assurance. However, the modules can be used for anonymizing data sets, preserving data characteristics, such as uniqueness and consistency. The data formats of, for example, email addresses, urls, dates, are also preserved


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